• Health Policy and Management
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• v.31 no.2
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• pp.148-157
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• 2021

Background: Based on that the key function of health technology is improving the quality of healthcare services, our study purports to explore the process of medical device development in detail and to discuss its policy implications. Methods: A total of 12 in-depth interviews were conducted with four groups of industry, hospital, academia, and civil society. All of the interviewees except those from civil society were involved in the new medical device development between 2009 and 2018. We performed a text network analysis and content analysis of the interview data. Results: The frequency and the degree centrality rankings suggested a close association between the utilization issue and the technology development. Similarly, the results of the content analysis showed that the appropriate intervention in the utilization of technology has a direct impact on the progress of development. Under the continuous industrial effort to boost profits by developing new technology, service providers and citizens should be knowledgeable of and make good use of the new technology for the provision of better services. Conclusion: As the development itself would not guarantee the improvement of service quality and better health outcomes, health technology policies should take a more comprehensive view to serve the unmet needs and even to facilitate the technology development.

• Journal of The Korean Association of Information Education
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• v.26 no.6
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• pp.553-566
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• 2022

This study collects social data and academic research data from portal sites and RISS, and analyzes TF-IDF, N-Gram, semantic network analysis, and CONCOR analysis to analyze the social awareness and current aspects of 'AI Literacy' and 'AI Convergence Education'. Through this, we tried to understand the social awareness aspect and the current situation, and to suggest implications and directions. In the social data, the collection of 'AI Convergence Education' was more than twice that of 'AI Literacy', indicating that awareness of 'AI Literacy' was relatively low. In 'AI Literacy', the keyword 'human' in social data showed no cluster to which it belonged, indicating a lack of philosophical interest in and awareness of humanities and AI. In addition, the keyword 'Ministry of Education' showed high frequency, importance, and centrality of connection only in the social data of 'AI convergence education', confirming that 'AI convergence education' is closely related to government policy.

• Journal of KIISE:Databases
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• v.37 no.6
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• pp.343-350
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• 2010

We can get answers we want to know via questioning in Knowledge Search Service (KSS) based on Q&A Community. However, it is getting more difficult to find credible documents in enormous documents, since many anonymous users regardless of credibility are participate in answering on the question. In previous works in KSS, researchers evaluated the quality of documents based on textual information, e.g. recommendation count, click count and non-textual information, e.g. answer length, attached data, conjunction count. Then, the evaluation results are used for enhancing search performance. However, the non-textual information has a problem that it is difficult to get enough information by users in the early stage of Q&A. The textual information also has a limitation for evaluating quality because of judgement by partial factors such as answer length, conjunction counts. In this paper, we propose the QualityRank algorithm to improve the problem by textual and non-textual information. This algorithm ranks the relevant and credible answers by considering textual/non-textual information and user centrality based on Social Network Analysis(SNA). Based on experimental validation we can confirm that the results by our algorithm is improved than those of textual/non-textual in terms of ranking performance.

• Journal of Internet Computing and Services
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• v.24 no.1
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• pp.71-86
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• 2023

The purpose of this exploratory study is to compare the differences in public perceptual characteristics of Korean and Chinese societies regarding the metaverse using big data analysis. Due to the environmental impact of the COVID-19 pandemic, technological progress, and the expansion of new consumer bases such as generation Z and Alpha, the world's interest in the metaverse is drawing attention, and related academic studies have been also in full swing from 2021. In particular, Korea and China have emerged as major leading countries in the metaverse industry. It is a timely research question to discover the difference in social awareness using big data accumulated in both countries at a time when the amount of mentions on the metaverse has skyrocketed. The analysis technique identifies the importance of key words by analyzing word frequency, N-gram, and TF-IDF of clean data through text mining analysis, and analyzes the density and centrality of semantic networks to determine the strength of connection between words and their semantic relevance. Python 3.9 Anaconda data science platform 3 and Textom 6 versions were used, and UCINET 6.759 analysis and visualization were performed for semantic network analysis and structural CONCOR analysis. As a result, four blocks, each of which are similar word groups, were driven. These blocks represent different perspectives that reflect the types of social perceptions of the metaverse in both countries. Studies on the metaverse are increasing, but studies on comparative research approaches between countries from a cross-cultural aspect have not yet been conducted. At this point, as a preceding study, this study will be able to provide theoretical grounds and meaningful insights to future studies.

• Journal of Korea Technology Innovation Society
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• v.17 no.1
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• pp.242-263
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• 2014

Establishment of efficient cooperative ecosystem of research institutes is important for the efficiency of national innovation system, especially in the era of technology convergence. Performance of institutes inside the ecosystem is dependent on the position of the institutes in the system. This study investigates the relationship between network structure and research performance, and determines significant factors on the research performance. The results of 5 year panel data analysis of SCI journal papers of Korean government research institutes indicate that four network centralities -degree, betweenness, closeness, and eigenvector- and structural holes have significant effect on the research performance of the institutes. Among the four centralities, closeness and eigenvectors are more significant than others. Implications of the results of this study for policy of establishing efficient cooperative system are that increasing the cooperative activities of less active institutes is more effective for research performance than increasing the magnitude of cooperative activities of all institutes. Also, when an institute starts a new cooperative relationship, it is better to have relationship with an active institute first.

• Journal of Science Education
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• v.40 no.3
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• pp.267-286
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• 2016

The study aimed to investigate a research trend in science gifted education of six years from 2010 to 2015 by utilizing literature analysis and Social Network Analysis (SNA) methods. In this study, 275 papers published in eight major academic journals of science education and gifted education were selected as research subjects. First, through the literature analysis, it was found that the most frequent research topics were cognitive characteristics (25.8%), curriculum/programs (22.6%), and social and emotional characteristics (20.2%). For the research method employed in research papers, the survey research (46.5%) was appeared as the most frequently employed method, and followed by experimental (18.8%), program development (10.6%), correlation (10.3%), and qualitative (6.4%) research methods. The most frequent research subject was appeared as middle school students (33.7%) and followed by elementary school (30.6%), and high school (12.7%) students. Second, the SNA method was utilized for producing keyword frequency, degree centrality and network analyses. It was appeared that the most common keywords over six years included 'science gifted', 'gifted education', and 'creativity' and frequent keywords were science gifted, gifted education, gifted, creativity, science inquiry, perception, (creative) problem solving, science high school, scientific attitude, and STEAM. Third, through 2-mode network analysis, it was found that the research papers about cognitive characteristics were mainly related to perceptions, thinking ability, scientific argumentation, science inquiry and so on. It was also found that the research papers about social and emotional characteristics were related to correlation, motivation, creativity-character, self-efficiency and so on. It was concluded that the SNA method can be performed with literature analysis together for better understandings and interpretations of the research trend of science gifted education in-depth.

• Journal of Life Science
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• v.26 no.8
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• pp.904-910
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• 2016

Complex traits are determined by the combined effects of many loci and are affected by gene networks or biological pathways. Systems biology approaches have an important role in the identification of candidate genes related to complex diseases or traits at the system level. The gene network analysis has been performed by diverse types of methods such as gene co-expression, gene regulatory relationships, protein-protein interaction (PPI) and genetic networks. Moreover, the network-based methods were described for predicting gene functions such as graph theoretic method, neighborhood counting based methods and weighted function. However, there are a limited number of researches in livestock. The present study systemically analyzed genes associated with 102 types of economic traits based on the Animal Trait Ontology (ATO) and identified their relationships based on the gene co-expression network and PPI network in cattle. Then, we constructed the two types of gene network databases and network visualization system (http://www.nabc.go.kr/cg). We used a gene co-expression network analysis from the bovine expression value of bovine genes to generate gene co-expression network. PPI network was constructed from Human protein reference database based on the orthologous relationship between human and cattle. Finally, candidate genes and their network relationships were identified in each trait. They were typologically centered with large degree and betweenness centrality (BC) value in the gene network. The ontle program was applied to generate the database and to visualize the gene network results. This information would serve as valuable resources for exploiting genomic functions that influence economically and agriculturally important traits in cattle.

• Journal of Korean Library and Information Science Society
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• v.50 no.1
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• pp.23-41
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• 2019

This study understand the subject domain of book which has been read by men and woman elderly by analizying the PFNET using library big data and confirm the difference between adult at age 30-40. This study extract co-occurrence matrix of book lending on the popular book list from library big data, for 4 group, men/woman elderly, men/woman adult. With these matrix, this study performs FP network analysis. And Pearson Correlation Analysis based on the Triangle Betweenness Centrality calculated on the loan book was performed to understand the correlation among the 4 clusters which has been created by PNNC algorithm. As a result, reading trend which has been focused on modern korean novel has been revealed in elderly regardless gender, among them, men elderly show extreme tendency concentrated on modern korean long series novel. In the correlation analysis, the male elderly showed a weak negative correlation with the adult male of r = -0.222, and the negative direction of all the other groups showed that the tendency of male elderly's loan book was opposite.

• The Journal of Society for e-Business Studies
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• v.21 no.2
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• pp.23-46
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• 2016

With the increasing socio-economic value of an open collaboration over the Internet, it has become significantly important to successfully manage open source software development program. Most of the previous research have focused on various factors that influence the performance of the project, but studies on how the project owners recognized as "leader" affect the outcome of the project are very limited. This research investigates how individual and governance characteristics of an owner influences the performance of project based on impression formation and social capital theory. For a data set, we collect 611 Repositories and the owner's data from the open source development platform Github, and we form knowledge sharing network of an each repository by using social network analysis. We use hierarchical regression analysis, and our results show that a leader, who exposes a lot of one's personal information or who has actively followed and showed interests to communicate with other developers, affects positive impacts on project performance. A leader who has a high centrality in knowledge sharing network also positively affects on project performance. On the other hand, if a leader was highly willing to accept external knowledge or is recognized as an expert in the community with large numbers of followers, the result show negative impacts on project performance. The research may serve as a useful guideline not only for the future open source software projects but also for the effective management of different types of open collaboration.

• Health Communication
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• v.12 no.1
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• pp.51-72
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• 2017

Purpose: Adolescents use emoticons to express their emotions in an online environment. Hence, medical experts can understand the emotions of adolescents by emoticons. The goal of this study was to investigate the relationship between various emotions and emoticons among the Korean adolescents. Methods: The questionnaire survey was conducted between September 1 and 30, 2014, involving 3,272 students in elementary schools, middle schools, and high schools affiliated in the Department of Education of the metropolitan city of Busan. A total of 1,717 students responded to the survey. The participants consisted of 806 males (46.9%), and 911 females (53.1%). Among these, there were 557 elementary school students (32.4%), 617 middle school students (35.9%), and 543 high school students (31.6%). A social networking analysis was conducted using NodeXL. Results: The frequency of emoticon use among adolescents runs in the order of joy, sadness, fear, surprise, anger, disgust, and then depression. Elementary school females mainly use emoticons to express joy; middle school females use emoticons to express sadness, surprise, anger, disgust, and depression; and high school females use emoticons to express fear. Age- and gender-specific emoticon networks were visualized by using the Haren-Korel fast multiscale algorithm. Commonly used emoticons by age and gender were expressed in the networks. Results of age- and gender-specific emoticon networks visualization show similar results of centrality of seven emoticons. Conclusion: In the digital communication environment, emoticons could be used to catch the emotions of adolescents in Korea.